!pip install plotly

Collecting plotly
  Downloading plotly-5.5.0-py2.py3-none-any.whl (26.5 MB)
Requirement already satisfied: six in c:\users\dell\anaconda3 2021\lib\site-packages (from plotly) (1.16.0)
Collecting tenacity>=6.2.0
  Downloading tenacity-8.0.1-py3-none-any.whl (24 kB)
Installing collected packages: tenacity, plotly
Successfully installed plotly-5.5.0 tenacity-8.0.1

import numpy as np
import pandas as pd
import plotly.express as px

px

<module 'plotly.express' from 'C:\\Users\\DELL\\anaconda3 2021\\lib\\site-packages\\plotly\\express\\__init__.py'>

confirmed_df = pd.read_csv("time_series_covid19_confirmed_global.csv")

confirmed_df

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	1/5/22	1/6/22	1/7/22	1/8/22	1/9/22	1/10/22	1/11/22	1/12/22	1/13/22	1/14/22
0	NaN	Afghanistan	33.939110	67.709953	0	0	0	0	0	0	...	158245	158275	158300	158309	158381	158394	158471	158511	158602	158639
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	212021	213257	214905	214905	219694	220487	222664	224569	226598	228777
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	220415	220825	221316	221742	222157	222639	223196	223806	224383	224979
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	25289	25289	26408	26408	26408	27983	28542	28899	28899	29888
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	86636	87625	88775	89251	89718	90316	91148	91907	92581	93302
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
275	NaN	Vietnam	14.058324	108.277199	0	2	2	2	2	2	...	1817721	1843563	1859841	1876394	1899575	1914393	1930428	1958719	1975444	1991484
276	NaN	West Bank and Gaza	31.952200	35.233200	0	0	0	0	0	0	...	471414	471414	471414	471414	471414	472910	473302	473302	474234	474234
277	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	10146	10152	10159	10178	10197	10197	10208	10220	10233	10233
278	NaN	Zambia	-13.133897	27.849332	0	0	0	0	0	0	...	269991	274087	278276	281576	282904	284389	287110	289415	291582	293695
279	NaN	Zimbabwe	-19.015438	29.154857	0	0	0	0	0	0	...	219057	220178	221282	221918	221918	223000	223000	224433	225084	225637

280 rows × 728 columns

confirmed_df = confirmed_df.drop(columns=['Lat', 'Long', 'Province/State'])

confirmed_df

	Country/Region	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	...	1/5/22	1/6/22	1/7/22	1/8/22	1/9/22	1/10/22	1/11/22	1/12/22	1/13/22	1/14/22
0	Afghanistan	0	0	0	0	0	0	0	0	0	...	158245	158275	158300	158309	158381	158394	158471	158511	158602	158639
1	Albania	0	0	0	0	0	0	0	0	0	...	212021	213257	214905	214905	219694	220487	222664	224569	226598	228777
2	Algeria	0	0	0	0	0	0	0	0	0	...	220415	220825	221316	221742	222157	222639	223196	223806	224383	224979
3	Andorra	0	0	0	0	0	0	0	0	0	...	25289	25289	26408	26408	26408	27983	28542	28899	28899	29888
4	Angola	0	0	0	0	0	0	0	0	0	...	86636	87625	88775	89251	89718	90316	91148	91907	92581	93302
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
275	Vietnam	0	2	2	2	2	2	2	2	2	...	1817721	1843563	1859841	1876394	1899575	1914393	1930428	1958719	1975444	1991484
276	West Bank and Gaza	0	0	0	0	0	0	0	0	0	...	471414	471414	471414	471414	471414	472910	473302	473302	474234	474234
277	Yemen	0	0	0	0	0	0	0	0	0	...	10146	10152	10159	10178	10197	10197	10208	10220	10233	10233
278	Zambia	0	0	0	0	0	0	0	0	0	...	269991	274087	278276	281576	282904	284389	287110	289415	291582	293695
279	Zimbabwe	0	0	0	0	0	0	0	0	0	...	219057	220178	221282	221918	221918	223000	223000	224433	225084	225637

280 rows × 725 columns

confirmed_df = confirmed_df.groupby(by='Country/Region').aggregate(np.sum).T

confirmed_df

Country/Region	Afghanistan	Albania	Algeria	Andorra	Angola	Antigua and Barbuda	Argentina	Armenia	Australia	Austria	...	United Kingdom	Uruguay	Uzbekistan	Vanuatu	Venezuela	Vietnam	West Bank and Gaza	Yemen	Zambia	Zimbabwe
1/22/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1/23/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
1/24/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
1/25/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
1/26/20	0	0	0	0	0	0	0	0	4	0	...	0	0	0	0	0	2	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1/10/22	158394	220487	222639	27983	90316	5058	6399196	345981	1139097	1350225	...	14708999	456179	200730	7	447288	1914393	472910	10197	284389	223000
1/11/22	158471	222664	223196	28542	91148	5058	6533635	346224	1223505	1361741	...	14826336	463357	201155	7	447865	1930428	473302	10208	287110	223000
1/12/22	158511	224569	223806	28899	91907	5214	6664717	346811	1398776	1378747	...	14958196	473180	202473	7	449730	1958719	473302	10220	289415	224433
1/13/22	158602	226598	224383	28899	92581	5214	6793119	346811	1526961	1394599	...	15064685	483820	202473	7	449730	1975444	474234	10233	291582	225084
1/14/22	158639	228777	224979	29888	93302	5246	6932972	347084	1613249	1411421	...	15165209	490626	203297	7	452430	1991484	474234	10233	293695	225637

724 rows × 196 columns

confirmed_df.index.name = 'Date'
confirmed_df = confirmed_df.reset_index()

confirmed_df.tail()

Country/Region	Date	Afghanistan	Albania	Algeria	Andorra	Angola	Antigua and Barbuda	Argentina	Armenia	Australia	...	United Kingdom	Uruguay	Uzbekistan	Vanuatu	Venezuela	Vietnam	West Bank and Gaza	Yemen	Zambia	Zimbabwe
719	1/10/22	158394	220487	222639	27983	90316	5058	6399196	345981	1139097	...	14708999	456179	200730	7	447288	1914393	472910	10197	284389	223000
720	1/11/22	158471	222664	223196	28542	91148	5058	6533635	346224	1223505	...	14826336	463357	201155	7	447865	1930428	473302	10208	287110	223000
721	1/12/22	158511	224569	223806	28899	91907	5214	6664717	346811	1398776	...	14958196	473180	202473	7	449730	1958719	473302	10220	289415	224433
722	1/13/22	158602	226598	224383	28899	92581	5214	6793119	346811	1526961	...	15064685	483820	202473	7	449730	1975444	474234	10233	291582	225084
723	1/14/22	158639	228777	224979	29888	93302	5246	6932972	347084	1613249	...	15165209	490626	203297	7	452430	1991484	474234	10233	293695	225637

5 rows × 197 columns

confirmed_melt_df = confirmed_df.melt(id_vars='Date').copy()

confirmed_melt_df

	Date	Country/Region	value
0	1/22/20	Afghanistan	0
1	1/23/20	Afghanistan	0
2	1/24/20	Afghanistan	0
3	1/25/20	Afghanistan	0
4	1/26/20	Afghanistan	0
...	...	...	...
141899	1/10/22	Zimbabwe	223000
141900	1/11/22	Zimbabwe	223000
141901	1/12/22	Zimbabwe	224433
141902	1/13/22	Zimbabwe	225084
141903	1/14/22	Zimbabwe	225637

141904 rows × 3 columns

confirmed_melt_df.rename(columns={'value':'Confirmed'}, inplace=True)

confirmed_melt_df.head()

	Date	Country/Region	Confirmed
0	1/22/20	Afghanistan	0
1	1/23/20	Afghanistan	0
2	1/24/20	Afghanistan	0
3	1/25/20	Afghanistan	0
4	1/26/20	Afghanistan	0

max_date = confirmed_melt_df['Date'].max()
max_date

'9/9/21'

confirmed_melt_df['Date'] = pd.to_datetime(confirmed_melt_df['Date'])

confirmed_melt_df.head()

	Date	Country/Region	Confirmed
0	2020-01-22	Afghanistan	0
1	2020-01-23	Afghanistan	0
2	2020-01-24	Afghanistan	0
3	2020-01-25	Afghanistan	0
4	2020-01-26	Afghanistan	0

confirmed_melt_df['Date'] = confirmed_melt_df['Date'].dt.strftime('%m/%d/%Y')

max_date = confirmed_melt_df['Date'].max()
max_date

'12/31/2021'

Visualization

total_confirmed_df = confirmed_melt_df[confirmed_melt_df['Date'] == max_date]
total_confirmed_df

	Date	Country/Region	Confirmed
709	12/31/2021	Afghanistan	158084
1433	12/31/2021	Albania	210224
2157	12/31/2021	Algeria	218432
2881	12/31/2021	Andorra	23740
3605	12/31/2021	Angola	81593
...	...	...	...
138993	12/31/2021	Vietnam	1731257
139717	12/31/2021	West Bank and Gaza	469748
140441	12/31/2021	Yemen	10126
141165	12/31/2021	Zambia	254274
141889	12/31/2021	Zimbabwe	213258

196 rows × 3 columns

total_confirmed = total_confirmed_df['Confirmed'].sum()
total_confirmed

288230968

fig = px.bar(total_confirmed_df, x='Country/Region', y='Confirmed')
fig.show()

fig = px.bar(total_confirmed_df.sort_values('Confirmed', ascending=False).head(30)
             , x='Country/Region', y='Confirmed', text = 'Confirmed')
fig.show()

fig2 = px.scatter(confirmed_melt_df, x='Date', y='Confirmed', color='Country/Region')
fig2.show()

fig3 = px.line(confirmed_melt_df[confirmed_melt_df['Country/Region'] == 'Bangladesh'], x='Date', y='Confirmed')
fig3.show()